Systemantics Book Notes

I probably wonโ€™t be reading another book about systems theory after this one. I canโ€™t say I loved it. It felt like it was written by some AI that was translating math directly into English. 

That being said I think this book represents an interesting take on the world.

Instead of looking at the world through our own subjective lenses and interpreting everything around us a linear narrative Systemantics urges the reader to see the world through a new perspective – that of systems.

Systems are all around us, we are part of an infinite # of systems.

Letโ€™s refresh a couple of concepts before we start that will help define what a system is:

  • System ๐Ÿ‘‰ An interconnected set of relationships that requires individual agents, creating inflows and outflows of information. Systems can scale up to infinite complexity.  and knowing where they end or begin is hard.  No one, these days, can avoid contact with Systems.
  • Agent  ๐Ÿ‘‰ an individual node that can act to effect the state of that relationship modifying the inflows and outflows
  • Complicated System  ๐Ÿ‘‰  Think of this as a mechanism device. Simple inflows, outflows and a very defined set of behaviours. It cannot generate its own results. Easier for us to understand. 
  • Complex System  ๐Ÿ‘‰ Acts like a biological system. Has a generator function, can exhibit new behaviour. Very difficult to understand. 

This book is an exploration of basic principles to help you shift from subjective thinking to systems thinking. 

First letโ€™s start with one of the best quotes of the bookโ€ฆ.

Stated as succinctly as possible: the fundamental problem does not lie in any particular System but rather in Systems As Such. Salvation, if it is attainable at all, even partially, is to be sought in a deeper understanding of the ways of all Systems, not simply in a criticism of the errors of a particular System.

We often look at a particular system and think โ€œwow this is so messed upโ€ but itโ€™s important to realize a lot of these problems are system agnostic. They would be happening even if we looked at another system, itโ€™s just how information gets organized.  ๐Ÿ—๏ธ

Systems function almost like an organism when they reach a certain scale.

As they move from a complicated system to a mechanism system they start to act unpredictably. They get harder for our linear brains to comprehend.

Alright enough definitions. Time to get our system(s) on. ๐Ÿ•บ

Human Cognition and Understanding Systems ๐Ÿ™

LARGE COMPLEX SYSTEMS ARE BEYOND HUMAN CAPACITY TO EVALUATE ๐Ÿค”

Humans can understand simple systems or complicated systems, something like a mechanical device is easy to understand. There are limited inflows/outflows, not much leverage and just a few agents.  Once you move to a complicated system that can produce its own results then our brains just canโ€™t grasp it. 

Weโ€™re trying to take a linear wetware computer to understand compounding exponential complexity. 

While we donโ€™t need to figure everything outโ€ฆ just knowing you canโ€™t and avoiding being overconfident is enough. 

NEW SYSTEMS MEAN NEW PROBLEMS ๐Ÿ™…

Like the title says, we often think the way to fix a system or problem is to add a new one. This is often faulty band-aid thinking that can cause more problems. 

THE BIGGER THE SYSTEM, THE NARROWER AND MORE SPECIALIZED THE INTERFACE WITH INDIVIDUALS ๐Ÿ“บ

The gradual fading-out of the individual as systems grow larger works like the physical structure of the brain. Agents act as individual neurons that propagate messages and rely narrative data to help the emergent consciousness understand what is going on. 

As an individual you have very small noticeable touch points with the greater system. Think economics, taxes and social trends. 

The System has its effects on the people within it. It isolates them, feeds them a distorted and partial version of the outside world, and gives them the illusion of power and effectiveness.

SYSTEMS ATTRACT SYSTEMS-PEOPLE ๐Ÿ‘จโ€๐Ÿ‘ฉโ€๐Ÿ‘ง

As a distribution function the majority of people will fit this definition as โ€œsystems peopleโ€ who want to exist within some specific well defined system of economics, education or interpersonal development.

These people are naturally attracted to systems and rewarded as such.  ๐Ÿ—ณ๏ธ

The System calls forth those attributes in its members and rewards the extreme degrees of them.

Systems Efficiency  โš™๏ธ

SYSTEMS IN GENERAL WORK POORLY OR NOT AT ALL ๐Ÿ“‰

All over the world, in great metropolitan centers as well as in the remotest rural backwaters, in sophisticated electronics laboratories and in dingy clerical offices, people everywhere are struggling with a Problem:[a. ][xii] THINGS ARENโ€™T WORKING VERY WELL

When you dig into any system there is an incredible amount of waste and inefficiency. Itโ€™s rare to see a system that works in perfect sync with everything else. They are clumsy and grossly inefficient.            

COMPLICATED SYSTEMS SELDOM EXCEED FIVE PERCENT EFFICIENCY ๐Ÿงฎ

Wowza on this one. Think of a big bureaucratic monster and this feels about right. Lean organizations can move fast and efficiency but as you increase the relational complexity things just donโ€™t work. Think about a big bank that needs to get 1005 sign offs from higher ups before they can do something really simple. 

SYSTEMS OFTEN OPERATE BACKWARDS โ†ช๏ธ

Things not only donโ€™t work out well, they work out in strange, even paradoxical ways. Our plans not only go awry, they produce results we never expected. Indeed, they often produce the opposite result from the one intended.

Insecticides – >  introduced to control disease and improve crop yields, end of accumulating in animals which poison them. Meanwhile, the insects develop immunity to insecticides, even learning to thrive on them.

3rd World Issues – > Many poorer nations, whose greatest need is food to feed their people, sell their crops and bankrupt themselves to buyโ€”not food, but advanced military hardware for the purpose of defending themselves against their equally poor neighbors, who are doing the same thing.

A COMPLEX SYSTEM THAT WORKS IS INVARIABLY FOUND TO HAVE EVOLVED FROM A SIMPLE SYSTEM THAT WORKED The parallel proposition also appears to be true:  A COMPLEX SYSTEM DESIGNED FROM SCRATCH NEVER WORKS AND CANNOT BE MADE TO WORK. YOU HAVE TO START OVER, BEGINNING WITH A WORKING SIMPLE SYSTEM ๐ŸŒฑ๐ŸŒฑ

This may be my favorite rule from the entire book. Complexity cannot be planned, it must be grown from lean roots that have been scaled up in a natural environment. 

Think of feedback complexity and antifragility. As a lean system (or simple system) grows itโ€™s subject to immense amounts of volatility and stress, the system adapts and becomes antifragile. If itโ€™s lean itโ€™s able to do this more effectively. Basing growth and planning of larger systems can work if the simple – > complex model principles are followed. 

Tricky Tricky Systems ๐Ÿ˜ˆ

SYSTEMS TEND TO OPPOSE THEIR OWN PROPER FUNCTIONS ๐Ÿ™…

Think of Goodhartโ€™s law – when a measure becomes the purpose of a system it ceases to be a good measure. The entire system starts to manipulate and game the metric focusing just on that singular goal creating a cancerous system.

Think – social hierarchy or greed

PEOPLE IN SYSTEMS DO NOT DO WHAT THE SYSTEM SAYS THEY ARE DOING ๐Ÿ˜•

The vast majority of people in an org or company have no essential purpose. They are fairly useless and redundant. Most progress is a result of Pareto principle, complex systems output, leverage and a host of other factors. At a certain size having many useless agents is normal. 

IN COMPLEX SYSTEMS, MALFUNCTION AND EVEN TOTAL NON-FUNCTION MAY NOT BE DETECTABLE FOR LONG PERIODS, IF EVER ๐Ÿ™ˆ

The problem of evaluating โ€œsuccessโ€ or โ€œfailureโ€ as applied to large Systems is compounded by the difficulty of finding proper criteria for such evaluation. What is the System really supposed to be doing?

There are serious measurement problems here. How do you define a goal? How to you weigh costs or risks compared to returns? What about slippage?

Murphyโ€™s Law, as it appears on the walls of most of the worldโ€™s scientific laboratories: IF ANYTHING CAN GO WRONG, IT WILL ๐Ÿ™ˆ๐Ÿ™ˆ

At a big enough size and complexity, all things that can possibly go wrong as a function of probability will eventually happen.

WHEN BIG SYSTEMS FAIL, THE FAILURE IS OFTEN BIG ๐Ÿ™ˆ๐Ÿ™ˆ๐Ÿ™ˆ

Self-evident or not, this cautionary Axiom is often overlooked or forgotten in the excited pursuit of grandiose goals by means of overblown Systems. What is ignored is the fact that big Systems represent a lumping-up of catastrophic potential.

Rolled up leverage = oversized results  Think 2008

Or think of Communism. 

A SYSTEM CAN FAIL IN AN INFINITE NUMBER OF WAYS ๐Ÿ™ˆ๐Ÿ™ˆ๐Ÿ™ˆ๐Ÿ™ˆ

As complexity of a system rises, one small change can wreck oversized destruction if the response is highly leveraged or can affect massive scale change. 

System Exploitation as a Natural Function ๐Ÿ˜ˆ

DESIGNERS OF SYSTEMS TEND TO DESIGN WAYS FOR THEMSELVES TO BYPASS THE SYSTEM  ๐Ÿ˜

The politicians who design systems often do so with their own motives and values in mind. This creates an agency dilemma where privileges individuals arenโ€™t able to bypass system rules. 

Think accredited investors only being able to invest meanwhile everyone is still allowed to gamble.         

IF A SYSTEM CAN BE EXPLOITED, IT WILL BE ๐Ÿ˜๐Ÿ˜

This has been proven a 1000 times in history. Important distinction here – exploitation is a natural outcome of a system that IS exploitable. Creating systems meditated by code and multiple parties of on chain accountable โ€œcanโ€ mitigate this 

PUSHING ON THE SYSTEM DOESNโ€™T HELP ๐Ÿ˜๐Ÿ˜๐Ÿ˜

You canโ€™t change shit. Especially big stuff

WHATEVER THE SYSTEM HAS DONE BEFORE, YOU CAN BE SURE IT WILL DO IT AGAIN ๐Ÿ˜๐Ÿ˜๐Ÿ˜๐Ÿ˜

Just sayingโ€ฆ itโ€™s gonna repeat. Thatโ€™s why history is so important

Emergent Systems Behavior  ๐Ÿ‘ป

COMPLEX SYSTEMS EXHIBIT UNEXPECTED BEHAVIOR  ๐Ÿ˜ฏ

This can perhaps be clarified by saying that, no matter what else it does, a System will act like a System. We are accustomed to thinking that a System acts like a machine,[b. ] and that if we only knew its mechanism, we could understand, even predict, its behavior. This is wrong. Systems at big enough scale function have a generator function like a biological system that can create highly unexpected behaviour. 

THE SYSTEM IS A LAW UNTO ITSELF ๐Ÿ˜จ

When a System continues to do its own thing, regardless of circumstances, we may be sure it is acting in pursuit of inner goals. This observation leads us, by a natural extension, to the insight that SYSTEMS DEVELOP GOALS OF THEIR OWN THE INSTANT THEY COME INTO BEING 

Furthermore, it seems axiomatically clear that: INTRASYSTEM GOALS COME FIRST

Ghost in the shell anyone?

This idea of a generator function for sufficiently complex systems keeps coming back again and again. Unbounded unexpected complexity. 

SYSTEMS DONโ€™T WORK FOR YOU OR FOR ME. THEY WORK FOR THEIR OWN GOALS ๐Ÿ™ˆ

Prior to and underlying any other Goal, the System seems to have a blind, instinctive urge to maintain itself.

Agents that maintain a system (may not be away of) function to protect the inner goals of the system. Knowledge of these inner goals may not be known or may be a result of algorithmic bias scaled out via technology. 

THE SYSTEM BEHAVES AS IF IT HAS A WILL TO LIVE  ๐Ÿค–

The goals of the system becomes intertwined with the goals of countless agents and their individual goals. A large scale system exists within a symbiotic relationship with its agents which can manifest in displaying its own unique will. 

How to Level Up Your System ๐ŸŽฎ

CHERISH YOUR BUGS. STUDY THEM ๐Ÿ›๐Ÿ›

Sometimes they represent a spontaneous offering of unsuspected capabilities of the System, a generous revelation of hidden vistas of alternative functioning not contemplated in the design specifications.

Our mind works by error-correction, but we donโ€™t want to know about our errors, we only have eyes for the goal.

Mistakes in a system show us how itโ€™s working, how to improve it or how to avoid huge downside. 

AS SYSTEMS EXPAND, NEW FUNCTIONS APPEAR SUDDENLY, IN STEPWISE FASHION ๐Ÿ‘‹

Again this idea of complexity keeps coming up, we canโ€™t estimate all the ways in which a system can change or manifest new outputs.